o iK @sddlZddlmZddlmZmZmZmZmZddl m Z m Z eeegdgdddddddd eeggd dd d gdddd gZ ej ejejejejgd d dgdddddd ej ejejejejgd gdddddddd gZejde e ddZejdee ddZdS)N)metrics)BaggingClassifierBaggingRegressorIsolationForestStackingClassifierStackingRegressor)assert_docstring_consistencyskip_if_no_numpydoc max_samplesFz4The number of samples to draw from X to train each.*) objectsinclude_paramsexclude_params include_attrs exclude_attrsinclude_returnsexclude_returnsdescr_regex_pattern ignore_types)cvn_jobs passthroughverboseTfinal_estimator_)r r r rrrrraverage zero_division a/This parameter is required for multiclass/multilabel targets\. If ``None``, the metrics for each class are returned\. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only report results for the class specified by ``pos_label``\. This is applicable only if targets \(``y_\{true,pred\}``\) are binary\. ``'micro'``: Calculate metrics globally by counting the total true positives, false negatives and false positives\. ``'macro'``: Calculate metrics for each label, and find their unweighted mean\. This does not take label imbalance into account\. ``'weighted'``: Calculate metrics for each label, and find their average weighted by support \(the number of true instances for each label\)\. This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall\.[\s\w]*\.* ``'samples'``: Calculate metrics for each instance, and find their average \(only meaningful for multilabel classification where this differs from :func:`accuracy_score`\)\.casecCtdi|dS)z@Check docstrings parameters consistency between related classes.Nrrrrp/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/sklearn/tests/test_docstring_parameters_consistency.py test_class_docstring_consistencyfr"cCr)zBCheck docstrings parameters consistency between related functions.Nrrr rrr!#test_function_docstring_consistencymr#r$)pytestsklearnrsklearn.ensemblerrrrrsklearn.utils._testingrr !CLASS_DOCSTRING_CONSISTENCY_CASESprecision_recall_fscore_supportf1_score fbeta_scoreprecision_score recall_scorejoinsplit$FUNCTION_DOCSTRING_CONSISTENCY_CASESmark parametrizer"r$rrrr!s|   >